How can LLMs optimize robot soccer team movement?
Towards Learning Scalable Agile Dynamic Motion Planning for Robosoccer Teams with Policy Optimization
This paper explores dynamic motion planning for multiple agents, like a robot soccer team, navigating a changing environment with obstacles. It proposes a learning-based model trained with policy optimization to allow agents to reach targets while minimizing collisions. The model uses a neural network that considers agent locations, target locations, and nearby obstacle information. Key limitations include scalability with increasing numbers of agents and obstacles and the current implementation's reliance on full observability (knowing the state of all agents and obstacles). Relevance to LLM-based multi-agent systems comes from the potential to replace the neural network motion planner with an LLM, enabling more complex reasoning and coordination between agents based on higher-level strategies and potentially symbolic knowledge. Further research directions include using graph neural networks for improved scalability and incorporating adversarial game theory for more realistic multi-agent scenarios.